Exploring Trends for Amherst College Emergency Medical Service
Introduction
This project uses data from the Amherst College Emergency Medical Service (ACEMS) to explore trends in campus medical emergencies. We wanted to pick a topic which could convey some information about Amherst, hoping that a more personal topic would lead to interesting findings. The decision to use ACEMS data began with a conversation between the two of us. With one of us a senior member of ACEMS and the other an unaffiliated Amherst student, we noticed a strong disconnect in the amount of campus knowledge and awareness of ACEMS (Julius didn’t even know how many members there were!). And given Austin’s connection to the team, accessing data and creating informed statistics would be much easier. We hope that this topic will both increase knowledge of ACEMS’ activities and generally reflect some trends of Amherst’s campus life.
To tackle these goals, we address three central questions. First, where and when do campus emergencies typically occur? To answer, we summarize ACEMS responses, tracking the locations and types of calls which are most prevalent. Second, how can we use ACEMS data to measure COVID policy? We explore how effective restrictions were on minimizing social interaction — using the frequency of alcohol and narcotics calls as a proxy for campus socialization. Third, what is the purview of ACEMS? When should a responder expect to escalate a call to the Amherst Fire Department (AFD)? We analyze which types of calls are typically escalated.
Discussion of our data and findings will begin with explanation of data collection and wrangling. Then we will explore our three central goals. Lastly, we conclude and discuss implications for campus life.
Methodology
Data Collection
Content
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DT package
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below just for demonstration.
library(DT)
mtcars %>%
select(mpg, cyl, hp) %>%
datatable(colnames = c("MPG", "Number of cylinders", "Horsepower"),
filter = 'top',
options = list(pageLength = 10, autoWidth = TRUE))kableExtra package
You can also use kableExtra for customizing HTML tables.
library(kableExtra)
summary(cars) %>%
kbl(col.names = c("Speed", "Distance"),
row.names = FALSE) %>%
kable_styling(bootstrap_options = "striped",
full_width = FALSE) %>%
row_spec(0, bold = TRUE) %>%
column_spec(1:2, width = "1.5in") | Speed | Distance |
|---|---|
| Min. : 4.0 | Min. : 2.00 |
| 1st Qu.:12.0 | 1st Qu.: 26.00 |
| Median :15.0 | Median : 36.00 |
| Mean :15.4 | Mean : 42.98 |
| 3rd Qu.:19.0 | 3rd Qu.: 56.00 |
| Max. :25.0 | Max. :120.00 |
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This is a figure caption
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